Self-service product return using computer vision and artificial intelligence

ABSTRACT

Systems and methods for returning an item. The methods comprise: performing item return operations by a computing device using at least one of machine learned information about a person who purchased the item, machine learned information about a person returning the item, and machined learned information about a condition of the item at the time of sale and at the time of return; and automatively sorting the item using a conveyer system to move the item from a counter to a respective storage area of a plurality of storage areas assigned to different product types.

BACKGROUND

Statement of the Technical Field

The present disclosure relates generally to computing systems. Moreparticularly, the present disclosure relates to implementing systems andmethods for self-service product return using computer vision andArtificial Intelligence (“AI”).

DESCRIPTION OF THE RELATED ART

Today when you want to return an item to a retail store you need to findthe receipt, take the item to the store, stand in a long line, and thenexplain why you are returning the product. This is a slow andinefficient process. It costs the store a lot of money to have anemployee assist in the return process. In addition, the process does nothave much security and thieves return products that are damaged or thatthey did not pay for. Some companies have implemented ideas like QuickResponse (“QR”) codes to help speed up the process. However, thisprocess is still expensive since an employee is still needed to assistin the return process.

SUMMARY

The present disclosure concerns implementing systems and methods forreturning an item. The methods comprise: performing item returnoperations by a computing device using at least one of machine learnedinformation about a person who purchased the item, machine learnedinformation about a person returning the item, and machined learnedinformation about a condition of the item at the time of sale and/or atthe time of return; and automatively sorting the item using a conveyersystem to move the item from a counter to a respective storage area of aplurality of storage areas assigned to different product types.

In some scenarios, the methods also comprise: learning features andcharacteristics of counterfeit items which are not consistent withfeatures and characteristics of corresponding non-counterfeit items;determining if the item is a counterfeit item based on the learnedfeatures and characteristics of counterfeit items; allowing return ofthe item if it is determined that the item is not a counterfeit item;and denying the return of the item if it is determined that the item isa counterfeit item.

In those or other scenarios, the methods further comprise verifying thatthe item's return is authorized by (A) determining if a credit cardnumber, token or code obtained from a user matches that used to purchasethe item, or (B) determining if a person shown in an image captured by acamera located by a return station matches a person shown in an imagecaptured during a purchase transaction for the item. Imaging andscanning operations may also be performed to determine item relatedinformation comprising at least one of a brand of the item, a producttype for the item, a size of the item, a color of the item, anauthentication mark made on the item, a weight of the item, and a codeassociated with the item. A validation can be made that the item beingreturned is a previously purchased item based on the item relatedinformation. A validation can also or alternatively be made that theitem is not associated with a previous return attempt based on the itemrelated information.

In those or other scenarios, the condition of the item is determinedbased on contents of an image captured while the item is being returned.The item's condition is then used to determine if it can be resoldand/or if it was the same or different at a time of its purchase.

BRIEF DESCRIPTION OF THE DRAWINGS

The present solution will be described with reference to the followingdrawing figures, in which like numerals represent like items throughoutthe figures.

FIG. 1 is an illustration of an illustrative system.

FIG. 2 is an illustration of an illustrative computing device.

FIGS. 3A-3D (collectively referred to as “FIG. 3”) provide a flowdiagram of an illustration method for returning an item which waspurchased.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments asgenerally described herein and illustrated in the appended figures couldbe arranged and designed in a wide variety of different configurations.Thus, the following more detailed description of various embodiments, asrepresented in the figures, is not intended to limit the scope of thepresent disclosure, but is merely representative of various embodiments.While the various aspects of the embodiments are presented in drawings,the drawings are not necessarily drawn to scale unless specificallyindicated.

The present solution may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the present solution is, therefore,indicated by the appended claims rather than by this detaileddescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

Reference throughout this specification to features, advantages, orsimilar language does not imply that all of the features and advantagesthat may be realized with the present solution should be or are in anysingle embodiment of the present solution. Rather, language referring tothe features and advantages is understood to mean that a specificfeature, advantage, or characteristic described in connection with anembodiment is included in at least one embodiment of the presentsolution. Thus, discussions of the features and advantages, and similarlanguage, throughout the specification may, but do not necessarily,refer to the same embodiment.

Furthermore, the described features, advantages and characteristics ofthe present solution may be combined in any suitable manner in one ormore embodiments. One skilled in the relevant art will recognize, inlight of the description herein, that the present solution can bepracticed without one or more of the specific features or advantages ofa particular embodiment. In other instances, additional features andadvantages may be recognized in certain embodiments that may not bepresent in all embodiments of the present solution.

Reference throughout this specification to “one embodiment”, “anembodiment”, or similar language means that a particular feature,structure, or characteristic described in connection with the indicatedembodiment is included in at least one embodiment of the presentsolution. Thus, the phrases “in one embodiment”, “in an embodiment”, andsimilar language throughout this specification may, but do notnecessarily, all refer to the same embodiment.

As used in this document, the singular form “a”, “an”, and “the” includeplural references unless the context clearly dictates otherwise. Unlessdefined otherwise, all technical and scientific terms used herein havethe same meanings as commonly understood by one of ordinary skill in theart. As used in this document, the term “comprising” means “including,but not limited to”.

In retail stores, there is a need for a way to return purchased itemswithout a requirement for store employee assistance. Accordingly, thepresent solution provides an automated way for items to be returned toretail stores or other business entities (e.g., libraries). Multiplesystems are used to speed up the return process and to verify theauthenticity and quality of the returned products. These systemscomprise cameras. Cameras have improved in quality and price over theyears. In addition, AI and machine learning allow cameras to: identify aperson, receipt, and/or credit card; determine the state or condition ofa product being returned; and/or determine the authenticity of theproduct being returned. Furthermore, the cost of storing data associatedwith past purchases and about the sold products allow companies to storedata that can be used to verify the validity of the return products.

The present solution will now be described in relation to the return ofpurchased items. The present solution is not limited in this regard. Thepresent solution can also be used in loaned or borrowed itemapplications.

Referring now to FIG. 1, there is provided an illustration of anillustrative system 100. System 100 is generally configured tofacilitate the purchase of items and/or the return of purchased items136. The items include perishable items (e.g., food) and/ornon-perishable items (e.g., apparel, appliances, automotive parts,beauty supplies, personal care items, books, consumer electronics,entertainment tickets, fashion accessories, footwear, office supplies,sports equipment, toys, video games, watches, glasses and/or jewelry).

As shown in FIG. 1, system 100 comprises one or more optional Point OfSale (“POS”) stations. POS stations are well known in the art, andtherefore will not be described herein. Any known or to be known POSstation can be used herein without limitation. The POS station includesa fixed POS station (e.g., a traditional checkout counter), aself-checkout kiosk, or a mobile POS (e.g., a smart phone). The POSstation(s) is(are) generally configured to facilitate the initiation ofa purchase transaction and the completion of the same. In somescenarios, a conventional POS station is modified to implement machinelearning technology. For example, hardware and/or software is providedwith a POS station that is configured to learn features/characteristicsof a purchaser, learn patterns of movement of the purchaser, and/orlearn features/characteristics/conditions of a purchased item. Thelearned information is stored in a datastore 124 for later use in anitem return process. Datastore 124 can include, but is not limited to, adatabase.

System 100 also comprises a return station 102, cameras 106, 128, andcomputing devices 110, 122 communicatively coupled to each other via anetwork 112 (e.g., the Internet). Cameras are well known in the art, andtherefore will not be described herein. Any known or to be known cameracan be used herein without limitation. For example, in some scenarios,3D cameras are employed. The cameras are generally configured to captureimages and/or videos of scenes in their Field Of Views (“FOVs”). Theterm “Field Of View” or “FOV”, as used herein, refers to the extent ofthe observable world that is captured at any given moment by a camera.Each FOV has a value less than or equal to one hundred eighty degrees(180°).

Camera 128 is placed at a location relative to the return station 102that is suitable for capturing images and/or videos of people 132 tryingto return items 136. Camera 128 is provided to assist in verifying thatthe same person who purchased the item is the same person who isreturning the item. In this regard, the camera 128 employs algorithms toidentify a person in its FOV and extract features of the identifiedperson. The extracted features are compared against features shown in animage captured at the time of a respective purchase transactionperformed by the POS station 180. If a match exists, then a verificationis made that the person is the same person who purchased the item. If amatch does not exist, then a flag can be set and/or store personnel canbe notified. Additionally or alternatively, the image of the differentperson captured at the time of return can be stored in a datastore 124so as to be associated with a user account, the respective purchasetransaction and/or the item return attempt.

Camera 106 is positioned above the return station 102 so that at least aportion of a counter 130 is in its FOV 108. Camera 106 is provided toassist in identifying items being returned and/or in determining theconditions of the items being returned. In this regard, the camera 106employs algorithms to determine what the item(s) is(are) on the counter130 (and in some scenarios on the weight scale 114 which is optional).For example, the camera 106 is able to identify an object in an imagecaptured thereby, determine characteristics of the object (e.g., color,size, shape, etc.), and determine a condition of the object (e.g.,damaged or resalable). The characteristics are then compared against adatabase of object-related data to determine if a match or a similarityexits therebetween. If a match or similarity exits, then the objectunique identifier associated with the matching or similar storedobject-related data is allocated to the image. The condition is alsocompared against a condition for the item shown in an image captured atthe time of its purchase. If a match does not exist, then a flag can beset that the item may not be resalable or was not sold in a damagedstate as suggested by the person returning the item. Store personnelcould be notified in either case.

Computing device 110 comprises a mobile computing device, such as atablet, personal computer or smart phone. Computing device 110 is usedby a person 132 to initiate an item return process, input informationinto system 100 during the item return process, and complete the itemreturn process. Accordingly, computing device 110 wirelesslycommunicates with an enterprise system 122, 124 via the network 112 foraccessing purchase transaction information generated by the POS station180 and notifying store personnel 134 of the item return process'sstatus. The enterprise system comprises a computing device 122 (e.g., aserver) and a datastore 124. The purchase transaction informationincludes, but is not limited to, identifiers for purchased items,dates/times of successful purchases, payment information, biometric datafor the people who made the purchases, voices of the people who made thepurchases, images of people who made the purchases, and/or videos of thepurchase transaction. The particulars of the item return process willbecome more evident as the discussion progresses.

The return station 102 comprises a counter 130 with a scanner 126, aweight scale 114 and a conveyer system 104 disposed therein so as to beaccessible for use during the item return process. The scanner caninclude, but is not limited to, a barcode scanner, an RFID tag scanner,or other Short Range Communication (“SRC”) enabled device (e.g., aBluetooth enabled device). The scanner is provided to acquire at leastone code from the item 136 being returned. The code can include, but isnot limited to, a Stock Keeping Unit (“SKU”) and/or a Unique ProductCode (“UPC”). SKUs and UPCs are well known in the art, and thereforewill not be described herein. The weight scale 114 is configured tomeasure the weight of an item 136 placed thereon. Barcode scanners, RFIDtag scanners, and weight scales are well known in the art, and thereforewill not be described herein. Any known or to be known barcode scanner,RFID tag scanner, and/or weight scale can be used herein withoutlimitation. Information generated by or obtained by components 114, 126is provided to a computing device 118 internal to the return station102. Computing device 118 is communicatively coupled to computing device110 and the enterprise system 122, 124 as well via network (although notshown in FIG. 1). Therefore, the information generated by or obtained bycomponents 114, 126 can be provided from computing device 118 tocomputing device 110, 122 for processing.

Computing device 118 is also configured to control operations of thescanner 126, weight scale 114 and/or the conveyer system 104. Conveyersystems are well known in the art, and therefore will not be describedin detail herein. Still, it should be understood that the conveyersystem 104 comprises mechanical handling equipment for moving items fromthe return counter 118 to storage bin(s) 116. With the assistance ofcomputing device 118, the items are directed to respective ones of thestorage bins 116 based on their product type. For example, a book isdirected to a first storage bin, while shampoo is directed to a seconddifferent storage bin. The storage bin(s) 116 provide(s) a means forstoring return items until the store employee 134 is ready to replacethem on a store floor for resale. This ability to automatively organizereturned items by type greatly improves a subsequent process for placingreturned items back on a store floor. In this regard, it should beunderstood that in conventional systems returned items are typicallyplaced in a single bin at the return station. As such, prior toreplacement of the same on a store floor, store personal must sort theitems in accordance with a store floor layout (e.g., all toiletry itemsare sorted into a first pile, while home goods are sorted into a secondpile) and/or conditions of the same (e.g., damaged items are sorted intoa third pile). In contrast, no such manual storing is required by thepresent solution. Accordingly, the present solution provides a more costeffective and efficient process for placing returned item back in astore floor.

The present solution employs machine learning techniques for variouspurposes. The machine learning techniques can be implemented by the POSstation(s) 180 and/or computing devices 110, 118, 122. For example, amachine learning algorithm is used to learn features and characteristicsof counterfeit items. Images of the real or original items captured by acamera at a checkout POS station 180 and images of the fake orcounterfeit items captured by camera 106 at the return station 102 canbe used here to detect and learn features and/or characteristics thereofwhich are not consistent with those of the corresponding original orreal non-counterfeit item. The features and/or characteristics caninclude, but are not limited to, stitching, label placement, labelorientation, coloration, texturing, material, and misspelling of brandnames. Detection of fake or counterfeit items has traditionally beenquite difficult and required the assistance of experts. The machinelearning aspect of the present solution provides an improved item returnprocess since (A) it eliminates the need for experts while stillensuring that counterfeit items will not be accepted for return (as hasbeen the case in some scenarios when store personnel manually handlesitem returns) and (B) continuously learns new features of counterfeititems so as to ensure real time updates are made in system 100 forlearned counterfeit item features/characteristics. Machine learningalgorithms are well known in the art, and therefore will not bedescribed herein. Any known or to be known machine learning algorithmcan be used herein without limitation. For example, supervised machinelearning algorithm(s), unsupervised machine learning algorithm(s) and/orsemi-supervised machine learning algorithm(s) are employed by system100.

The present solution is not limited to the architecture shown in FIG. 1.In this regard, it should be understood that system 100 can include moreor less components than that shown in FIG. 1. For example, in somescenarios, system 100 also comprises a printer for generating paperreceipts.

Referring now to FIG. 2, there is provided an illustration of anillustrative architecture for a computing device 200. Computing device110, 118, 122 of FIG. 1 is(are) the same as or similar to computingdevice 200. As such, the discussion of computing device 200 issufficient for understanding this component of system 100.

In some scenarios, the present solution is used in a client-serverarchitecture. Accordingly, the computing device architecture shown inFIG. 2 is sufficient for understanding the particulars of clientcomputing devices and servers.

Computing device 200 may include more or less components than thoseshown in FIG. 2. However, the components shown are sufficient todisclose an illustrative solution implementing the present solution. Thehardware architecture of FIG. 2 represents one implementation of arepresentative computing device configured to provide an improved itemreturn process, as described herein. As such, the computing device 200of FIG. 2 implements at least a portion of the method(s) describedherein.

Some or all components of the computing device 200 can be implemented ashardware, software and/or a combination of hardware and software. Thehardware includes, but is not limited to, one or more electroniccircuits. The electronic circuits can include, but are not limited to,passive components (e.g., resistors and capacitors) and/or activecomponents (e.g., amplifiers and/or microprocessors). The passive and/oractive components can be adapted to, arranged to and/or programmed toperform one or more of the methodologies, procedures, or functionsdescribed herein.

As shown in FIG. 2, the computing device 200 comprises a user interface202, a Central Processing Unit (“CPU”) 206, a system bus 210, a memory212 connected to and accessible by other portions of computing device300 through system bus 210, a system interface 260, and hardwareentities 214 connected to system bus 210. The user interface can includeinput devices and output devices, which facilitate user-softwareinteractions for controlling operations of the computing device 200. Theinput devices include, but are not limited, a physical and/or touchkeyboard 250. The input devices can be connected to the computing device200 via a wired or wireless connection (e.g., a Bluetooth® connection).The output devices include, but are not limited to, a speaker 252, adisplay 254, and/or light emitting diodes 256. System interface 260 isconfigured to facilitate wired or wireless communications to and fromexternal devices (e.g., network nodes such as access points, etc.).

At least some of the hardware entities 214 perform actions involvingaccess to and use of memory 212, which can be a Radom Access Memory(“RAM”), a disk driver and/or a Compact Disc Read Only Memory(“CD-ROM”). Hardware entities 214 can include a disk drive unit 216comprising a computer-readable storage medium 218 on which is stored oneor more sets of instructions 220 (e.g., software code) configured toimplement one or more of the methodologies, procedures, or functionsdescribed herein. The instructions 220 can also reside, completely or atleast partially, within the memory 212 and/or within the CPU 206 duringexecution thereof by the computing device 200. The memory 212 and theCPU 206 also can constitute machine-readable media. The term“machine-readable media”, as used here, refers to a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that store the one or more sets ofinstructions 220. The term “machine-readable media”, as used here, alsorefers to any medium that is capable of storing, encoding or carrying aset of instructions 220 for execution by the computing device 200 andthat cause the computing device 200 to perform any one or more of themethodologies of the present disclosure.

Computing device 200 implements machine learning technology. In thisregard, computing device 200 runs one or more software applications 222for facilitating the return of items. The software algorithms 222 usemachine learning algorithms 280 to learn characteristics of peopleassociated with purchase transactions and/or credit cards used forpayment during the purchase transactions, learn conditions of items atthe time of purchase, learn characteristics of original realnon-counterfeit items, learn characteristics or traits of counterfeititems, learn characteristic of times at the time of return, and/or learnsuspicious conduct indicating that a person is trying to return itemswhich have not been purchased or which are counterfeit. This learnedinformation can be used for various purposes as described herein. Forexample, an image of a person returning an item can be captured andprocessed to extract features of the person. The extracted features arecompared to learned features of a person associated with the purchasetransaction for the item and/or credit card used to purchase the item.The learned features were obtained using sensor data obtained during thepurchase transaction (e.g., captured images). Alternatively oradditionally, the authenticity of an item being returned can bedetermined based on the learned characteristics of original realnon-counterfeit items and/or the learned characteristics or traits ofcounterfeit items. Also, return abuses (e.g., free renting) are detectedusing learned conditions of an item at the time of purchase and at thetime of return. The present solution is not limited to the particularsof this example.

Referring now to FIG. 3, there is provided a flow diagram of anillustrative method 300 for returning an item which had been purchasedusing a POS station (e.g., POS station 180 of FIG. 1). As shown in FIG.3, method 300 comprises a plurality of operations 301-384. Method 300can include more or less operations than that shown in accordance with agiven application. Also, the present solution is not limited to theparticular order of the operations shown in FIG. 3. In this regard, itshould be understood that some or all of the operations can be performedin the same or different order than that shown.

Referring now to FIG. 3A, method 300 begins with 301 and continues with302 where a computing device (e.g., computing device 110 of FIG. 1)receives a user software interaction for starting an item returnprocess. This computing device can include, but is not limited to, atablet, a smart phone or other portable electronic device. A tether,chain or other linkage may be provided for securely coupling thecomputing device to a return station (e.g., return station 102 of FIG.1). This secure coupling ensures that the computing device will not bestolen, accidently misplaced, or damaged due to being dropped. The usersoftware interaction can include, but is not limited to, the selectionof an item from a drop down menu or the depression of a virtual buttonpresented on a display (e.g., display 254 of FIG. 2) of the computingdevice. In response to the user-software interaction, the item returnprocess is started in 304.

In 306, a Graphical User Interface (“GUI”) is presented to the user ofthe computing device. By way of the GUI, the user is prompted to specifya reason for returning an item (e.g., item 136 of FIG. 1). The reasonscan include, but are not limited to, defective, damaged, improper fit,wrong item, changed mind about item, and/or item arrived too late. In308, a user software interaction is performed to specify the reason forreturning the item. The user software interaction can include, but isnot limited to, entering text into a text box, selecting a reason from alist of reasons, selecting a box on the GUI, depressing a virtual buttonof the GUI, or using any other widget.

Next in 310, the user (e.g., person 132 of FIG. 1) is prompted for anindication as to whether (s)he has a receipt. The receipt can be ahardcopy (or printed) receipt or an electronic receipt. In response tothis prompt, the user performs a user-software interaction forindicating that (s)he has a receipt. The user-software interaction caninclude, but is not limited to, entering text in a text box, selecting abox on the GUI, depressing a virtual button of the GUI, or using anyother widget.

If the user does not have a receipt [312:NO], then method 300 continueswith 376-378 of FIG. 3D. As shown in FIG. 3D, 376-378 involve:outputting instructions from the computing device instructing the userto input identification information (e.g., a name, an address, a phonenumber, an account number, etc.) into the system (e.g., system 100 ofFIG. 1); and searching a datastore (e.g., datastore 124 of FIG. 1) forany purchase transaction information associated with the identificationinformation. If purchase transaction information is not found during thesearch [380:NO], then 384 is performed where one or more actions istaken. These actions include, but are not limited to, notifying the userthat no purchase transaction information was found, instructing the userto see store personnel, terminate the item return process, and/or returnto 302 of FIG. 3A. If purchase transaction information is found duringthe search [380:YES], then 382 is performed where method 300 goes to 314which will be described below.

Returning to FIG. 3A, if the user does have a receipt [312:YES], thenmethod 300 continues with 314-318. 314-318 involve: outputtinginstructions from the computing device instructing the user to place theprinted receipt on the counter (e.g., counter 130 of FIG. 1) at alocation in the FOV of a camera (e.g., camera 106 of FIG. 1) or place adisplay of an electronic device (e.g., a smart phone 182 of FIG. 1)showing the electronic receipt in the FOV of the camera; capturing animage of the receipt; and analyzing the receipt image to determine if acredit card or other non-cash payment was used to purchase the item.

If a credit card or other non-cash payment was used to purchase the item[320:YES], then 322-324 are performed. 322-324 involve: outputtinginstructions from the computing device instructing the user to place thecredit card in the FOV of the camera (e.g., camera 106 of FIG. 1); andcapturing an image of the credit card. Additionally or alternatively,322-324 involve: placing the electronic device (e.g., a smart phone 182of FIG. 1) in proximity to an SRC device (e.g., scanner 126 of FIG. 1)of the return station (e.g., return station 102 of FIG. 1); andobtaining a token or other code from the electronic device.

If a credit card was not used [320:NO], then optional 326-328 areperformed which involve: outputting instructions from the computingdevice instructing the user to face a camera (e.g., camera 128 of FIG.1); and capturing an image of the person's face.

Upon completing 324 or 328, method 300 continues with 330. In 330, theenterprise system (e.g., system 100) verifies that the item's return isauthorized by (A) determining if the credit card number, token or codeobtained in 324 matches that used to purchase the item or (B)determining if the person shown in the image captured in 328 matches theperson shown in an image captured by a POS station (e.g., POS station180 of FIG. 1) during the purchase transaction for the item. Thesedeterminations can be made via comparison operations performed by aremote server (e.g., computing device 122 of FIG. 1) using purchasetransaction data stored in a datastore (e.g., datastore 124 of FIG. 1).The purchase transaction data can include, but is not limited to, atime, a date, a list of purchased item identifiers, a price of eachpurchased items, a credit card number, a token, a code, and an image ofthe person(s) who purchased the items.

Subsequently, method 300 continues with 332 of FIG. 3B. As shown in FIG.3B, 332 involves determining whether authorization of the item's returnhas been verified. If not [332:NO], then 334 is performed where one ormore actions are taken. These actions can include, but are not limitedto, notifying the person that the item's return is not authorized,analyzing the person's conduct to determine if it is suspicious based onmachine learned patterns of suspicious conduct/movements and/or machinedlearned normal movement patterns for the person, and/or return to 306.Suspicious conduct can be detected by capturing a video of the personwhile (s)he is attempting to return the item, analyzing the video todetect a motion pattern specifying the person's movements, comparing themotion pattern to the machined learned patterns of suspiciousconduct/movements, and/or comparing the motion pattern to the machinedlearned normal movement patterns for the person. If a match existsbetween the motion pattern and at least one of the machined learnedpatterns of suspicious conduct/movements, then suspicious conduct isdetected. Otherwise suspicious conduct is not detected. If a matchexists between the motion pattern and the machined learned normalmovement patterns for the person, then suspicious conduct is notdetected. Otherwise suspicious conduct is detected.

If the item's return is authorized [332:YES], then 336 is performedwhere instructions are output from the computing device instructing theuser to place the item in proximity to a scanner (e.g., scanner 126 ofFIG. 1). In 338, the item is scanned to obtain at least one codetherefrom. The code can include, but is not limited to, a barcode, a SKUand/or a UPC. The code is processed in 340 to determine the brand of theitem and the type of item.

Next in 342, instructions are output from the computing device (e.g.,computing device 110 of FIG. 1) instructing the user to place the item(e.g., item 136 of FIG. 1) on a weight scale (e.g., weight scale 114 ofFIG. 1). The weight scale then measures the weight of the item in 344.The measured weight can be passed to an internal computing device (e.g.,computing device 118 of FIG. 1) of the return station 102 for storageand/or processing. The measured weight can be stored locally in a memory(e.g., memory 212 of FIG. 2) and/or remotely in a datastore (e.g.,datatore 124 of FIG. 1).

In 346, an image of the item is captured by a camera (e.g., camera 106of FIG. 1) while the item rests on the weight scale. The camera caninclude, but is not limited to, a 3D camera such that the item's heightis obtained in addition to its length and width. The captured image isprocessed in 348 to determine if it has a machined learned feature of acorresponding counterfeit item. This determination can be made byextracting features of the item from the captured image, and comparingthe extracted features to machined learned features of one or morecounterfeit items. If a match exits between the extracted features andthe machined learned features, store personnel is notified. If a matchdoes not exist between the extracted features and the machined learnedfeatures, method 300 continues with 350.

350 involves processing the image to determine a size and color of theitem, as well as identify any authentication mark made on the item.Next, operations are performed in 352 to validate that the item beingreturned is the previously purchased item. This validation is made bydetermining (A) if the item's size, color, weight and authenticationmark are consistent with an item having the brand and type specified bythe code obtained in 338, or (B) if the item is not associated with aprevious return attempt. Determination (B) can be made based on resultfrom comparing the code obtained in 338 to a code acquired in a previousreturn attempt. If a match exists, then it is determined that the itemis associated with a previous return attempt. Otherwise, a determinationis made that the item is not associated with a previous return attempt.

If a validation is not made that the item being returned is thepreviously purchased item [354:NO], then 356 is performed where one ormore actions are taken. These actions can include, but are not limitedto, notifying the user that the item is incorrect, and/or returning to336.

If a validation is made that the item being returned is the previouslypurchased item [354:YES], then method 300 continues with 358 of FIG. 3C.As shown in FIG. 3C, 358 involves analyzing the image of the item todetermine its condition. A determination is then made in 360 as towhether or not the item can be resold based on its condition. Forexample, a item can be resold if it is not damages and/or if itscondition is the same as that at the time of purchase. If the itemcannot be resold [362:NO], then 364 is performed where one or moreactions is taken. These actions can include, but are not limited to,notifying the person (e.g., person 132 of FIG. 1) that the item is notaccepted for return, notifying store personnel (e.g., store personnel134 of FIG. 1) that an item is not accepted for return based on a givenpolicy, store a reason code for the return failure, and/or go to 374which will be described below.

If the item can be resold [362:YES], then 366-372 are performed. 366-372involve: outputting instructions from the computing device (e.g.,computing device 110 of FIG. 1) instructing the user (e.g., person 132of FIG. 1) to place the item (e.g., item 136 of FIG. 1) on a conveyersystem (e.g., conveyer system 104 of FIG. 1); performing operations bythe conveyer system to direct the item to a storage area of a pluralityof storage areas (e.g., storage bins 116 of FIG. 1) based on the item'stype determined in previous 340; outputting a receipt. The receipt canbe a printed receipt or an electronic receipt. Subsequently, 374 isperformed where method 300 ends or other processing is performed.

The present solution can be used in various applications. In somescenarios, an image of the purchaser and/or associated metadata arerecorded when an item is purchased. This image and metadata can be usedto authenticate the person when (s)he returns the item to the store.This authentication is useful when the receipt is lost.

In other scenarios, an image of the purchaser and/or associated metadataare recorded when an item is purchased. Also, machine learningoperations are performed to (A) determine the state or condition of theitem at the time of purchase and (B) determine the state or condition ofthe item at the time of return. The state/condition of (B) is comparedto the state/condition of (A) when the item is returned. This could stopcases where someone returns an item and claims that the item was damageswhen they bought it or cases where someone tries to return a counterfeititem that doesn't match the item they purchased.

The present solution can also be used to stop fraudulent returns. Forexample, the present solution could stop cases where a person takes anitem of the retail floor and tries to return the item without anypurchase thereof. This could save retail stores a significant amount ofmoney in improper store credits.

In some scenarios, voice recognition is employed. System 100 wouldfurther comprise an audio microphone, a display, and an audio speaker.These additional components would be connected to the computing device110, computing device 118 and/or the enterprise system 122, 123. AIcould process the images and output questions on the display or from theaudio speaker. The customer could hold his(her) credit card in from of acamera 106, 128 so that a credit card number could be read therefrom.The customer could then be prompted to input additional authenticatinginformation such as a zip code associated with the credit card. Thiswould allow system 100 to be implemented with simple, low cost hardware.

Although the present solution has been illustrated and described withrespect to one or more implementations, equivalent alterations andmodifications will occur to others skilled in the art upon the readingand understanding of this specification and the annexed drawings. Inaddition, while a particular feature of the present solution may havebeen disclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. Thus, the breadth and scope of the presentsolution should not be limited by any of the above describedembodiments. Rather, the scope of the present solution should be definedin accordance with the following claims and their equivalents.

What is claimed is:
 1. A method for returning an item, comprising: performing item return operations by a computing device using at least one of machine learned information about a person who purchased the item, machine learned information about a person returning the item, and machined learned information about a condition of the item at at least one of the time of sale and at the time of return; and automatively sorting the item using a conveyer system to move the item from a counter to a respective storage area of a plurality of storage areas assigned to different product types.
 2. The method according to claim 1, further comprising performing operations by the computing device to learn features and characteristics of counterfeit items which are not consistent with features and characteristics of corresponding non-counterfeit items.
 3. The method according to claim 2, further comprising: determining if the item is a counterfeit item based on the learned features and characteristics of counterfeit items; allowing return of the item if it is determined that the item is not a counterfeit item; and denying the return of the item if it is determined that the item is a counterfeit item.
 4. The method according to claim 1, further comprising verifying by the computing device that the item's return is authorized by (A) determining if a credit card number, token or code obtained from a user matches that used to purchase the item, or (B) determining if a person shown in an image captured by a camera located by a return station matches a person shown in an image captured during a purchase transaction for the item.
 5. The method according to claim 1, further comprising performing imaging and scanning operations to determine item related information comprising at least one of a brand of the item, a product type for the item, a size of the item, a color of the item, an authentication mark made on the item, a weight of the item, and a code associated with the item.
 6. The method according to claim 5, further comprising validating that the item being returned is a previously purchased item based on the item related information.
 7. The method according to claim 5, further comprising validating that the item is not associated with a previous return attempt based on the item related information.
 8. The method according to claim 1, further comprising determining a condition of the item based on contents of an image captured while the item is being returned.
 9. The method according to claim 8, further comprising determining if the item can be resold based on the determined condition.
 10. The method according to claim 8, further comprising determining if the condition of the item is the same or different at a time of purchase and a time of return.
 11. A system, comprising: a processor; a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for returning items in a computing device, wherein the programming instructions comprise instructions to: perform item return operations using at least one of machine learned information about a person who purchased the item, machine learned information about a person returning the item, and machined learned information about a condition of the item at at least one of the time of sale and at the time of return; and a conveyer system configured to automatively sort the item using a conveyer system to move the item from a counter to a respective storage area of a plurality of storage areas assigned to different product types.
 12. The system according to claim 11, wherein the programming instructions further comprise instructions to learn features and characteristics of counterfeit items which are not consistent with features and characteristics of corresponding non-counterfeit items.
 13. The system according to claim 12, wherein the programming instructions further comprise instructions to: determine if the item is a counterfeit item based on the learned features and characteristics of counterfeit items; allow return of the item if it is determined that the item is not a counterfeit item; and deny the return of the item if it is determined that the item is a counterfeit item.
 14. The system according to claim 11, wherein the programming instructions further comprise instructions to verify that the item's return is authorized by (A) determining if a credit card number, token or code obtained from a user matches that used to purchase the item, or (B) determine if a person shown in an image captured by a camera located by a return station matches a person shown in an image captured during a purchase transaction for the item.
 15. The system according to claim 11, wherein the programming instructions further comprise instructions to perform imaging and scanning operations to determine item related information comprising at least one of a brand of the item, a product type for the item, a size of the item, a color of the item, an authentication mark made on the item, a weight of the item, and a code associated with the item.
 16. The system according to claim 15, wherein the programming instructions further comprise instructions to validate that the item being returned is a previously purchased item based on the item related information.
 17. The system according to claim 15, wherein the programming instructions further comprise instructions to validate that the item is not associated with a previous return attempt based on the item related information.
 18. The system according to claim 11, wherein the programming instructions further comprise instructions to determine a condition of the item based on contents of an image captured while the item is being returned.
 19. The system according to claim 18, wherein the programming instructions further comprise instructions to determine if the item can be resold based on the determined condition.
 20. The system according to claim 18, wherein the programming instructions further comprise instructions to determine if the condition of the item is the same or different at a time of purchase and a time of return. 